IoT Is More Cellular Then Ever

Forecast by Ericsson

In 2018, IoT devices will surpass mobile phones in cellular connectivity

By 2020, 70% of IoT devices will have cellular connectivity and their number will reach 1.5B connected devices

Exploitation of IoT devices and networks is already a reality

 

GSMA’s IoT Security Guidelines for Network Operators

Mobile network operators offering IoT services should include the following security mechanisms:

  • Secure Subscription Management Procedures 

  • Supply Management Protection  

  • IoT roaming protection

    • Roaming signaling storms/attacks protection

    • Security-based Steering of Roaming (SoR) 

    • Data Roaming Denial of Service

  • Protection of IoT Gateways

  • IoT Endpoint Device Blacklisting

  • Analytics-based Security

GSMA’s IoT Security Guidelines for Network Operators

Mobile network operators offering IoT services should include the following security mechanisms:

  • Secure Subscription Management Procedures 

  • Supply Management Protection  

  • IoT roaming protection

    • Roaming signaling storms/attacks protection

    • Security-based Steering of Roaming (SoR) 

    • Data Roaming Denial of Service

  • Protection of IoT Gateways

  • IoT Endpoint Device Blacklisting

  • Analytics-based Security

imVision’s AMP

IoT monitoring and security at the network level is currently the most cost-effective approach (although the analysis must be done as close as possible to the end point to avoid by-passing the monitoring points). Public network infrastructures are in a strategic position to offer such a monitoring and security services for IoT customers since they reside in the middle between IoT aggregation and the internet.

 

imVision’s real-time anomaly management solution protects IoT services against IoT malfunctioning or cyber threats by learning fine granular behavioral models for each device. The model correlates control and data planes and represent accurately the normal behavior of the service and is capable to detects deviations, which are indicative to operational malfunctional or security threats.

Learning Layer

  • Protocol Agnostic

  • Supervised Learning  - pre-defined knowledge

  • Unsupervised Learning – knowledge based on previous learning

imVision’s AMP

IoT monitoring and security at the network level is currently the most cost-effective approach (although the analysis must be done as close as possible to the end point to avoid by-passing the monitoring points). Public network infrastructures are in a strategic position to offer such a monitoring and security services for IoT customers since they reside in the middle between IoT aggregation and the internet.

 

imVision’s real-time anomaly management solution protects IoT services against IoT malfunctioning or cyber threats by learning fine granular behavioral models for each device. The model correlates control and data planes and represent accurately the normal behavior of the service and is capable to detects deviations, which are indicative to operational malfunctional or security threats.

Learning Layer

  • Protocol Agnostic

  • Supervised Learning  - pre-defined knowledge

  • Unsupervised Learning – knowledge based on previous learning

Analytics Layer

  • Anomalies reporting

  • Intelligent grouping of related events

  • Meaningful Insights, including severity & countermeasures 

Detection Layer

  •  Service aware detection –bottom up approach

  • Multiple Detection mechanisms:

    • Single Events

    • Volumetric Events

    • Content based

    • Context based

    • Dual-level detection

Remediation Layer

  • Automatic 3rd party firewall programming

  • Early warning

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